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<p>Threat detection in X-ray security screening is critical for preventing concealed threats in airports and other high-security venue where Improvised Explosive Devices (IEDs) remain among the most persistent and dangerous threats. The lack of a representative, and publicly available IED dataset has limited the development of machine-learning based automated threat detection systems. To address these issues, we propose an open access dataset, called IEDXray constructed for automated detection of IEDs. The dataset comprises 17,360 X-ray images captured under a strategic concealment protocol, covering scenarios ranging from isolated threats to heavily cluttered baggage environments. It includes diverse IED types—homemade explosives, batteries, and modified devices such as laptops, mobile phones, pagers, and walkie-talkies. To validate the dataset, we benchmark state-of-the-art detection models, including YOLOv10, Faster R-CNN, DETR, and GroundingDINO, establishing baseline results across multiple security-screening tasks. By reflecting real-world threat concealment, clutter, and variability, IEDXray provides the research community with a high-fidelity benchmark to advance automated explosive detection and improve x-ray security screening.</p>

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A Benchmark Dataset for Concealed Improvised Explosive Device Detection in X-ray Security Imaging

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月28日

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  • Natnael Abule Takele,
  • Divya Velayudhan,
  • Dwarikanath Mahapatra,
  • Hasan AlMarzouqi,
  • Naoufel Werghi

摘要

Threat detection in X-ray security screening is critical for preventing concealed threats in airports and other high-security venue where Improvised Explosive Devices (IEDs) remain among the most persistent and dangerous threats. The lack of a representative, and publicly available IED dataset has limited the development of machine-learning based automated threat detection systems. To address these issues, we propose an open access dataset, called IEDXray constructed for automated detection of IEDs. The dataset comprises 17,360 X-ray images captured under a strategic concealment protocol, covering scenarios ranging from isolated threats to heavily cluttered baggage environments. It includes diverse IED types—homemade explosives, batteries, and modified devices such as laptops, mobile phones, pagers, and walkie-talkies. To validate the dataset, we benchmark state-of-the-art detection models, including YOLOv10, Faster R-CNN, DETR, and GroundingDINO, establishing baseline results across multiple security-screening tasks. By reflecting real-world threat concealment, clutter, and variability, IEDXray provides the research community with a high-fidelity benchmark to advance automated explosive detection and improve x-ray security screening.